Overview

Dataset statistics

Number of variables13
Number of observations800214
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory79.4 MiB
Average record size in memory104.0 B

Variable types

Numeric13

Alerts

height is highly overall correlated with timestamp and 7 other fieldsHigh correlation
timestamp is highly overall correlated with height and 7 other fieldsHigh correlation
size is highly overall correlated with height and 8 other fieldsHigh correlation
tx_count is highly overall correlated with height and 8 other fieldsHigh correlation
difficulty is highly overall correlated with height and 7 other fieldsHigh correlation
median_fee_rate is highly overall correlated with avg_fee_rate and 3 other fieldsHigh correlation
avg_fee_rate is highly overall correlated with median_fee_rate and 2 other fieldsHigh correlation
total_fees is highly overall correlated with height and 11 other fieldsHigh correlation
fee_range_min is highly overall correlated with height and 7 other fieldsHigh correlation
fee_range_max is highly overall correlated with median_fee_rate and 3 other fieldsHigh correlation
input_count is highly overall correlated with height and 8 other fieldsHigh correlation
output_count is highly overall correlated with height and 8 other fieldsHigh correlation
output_amount is highly overall correlated with size and 6 other fieldsHigh correlation
median_fee_rate is highly skewed (γ1 = 783.5011544)Skewed
avg_fee_rate is highly skewed (γ1 = 447.0965554)Skewed
total_fees is highly skewed (γ1 = 79.18714208)Skewed
fee_range_min is highly skewed (γ1 = 100.8944991)Skewed
fee_range_max is highly skewed (γ1 = 349.1006345)Skewed
output_amount is highly skewed (γ1 = 56.51785642)Skewed
height is uniformly distributedUniform
height has unique valuesUnique
median_fee_rate has 178585 (22.3%) zerosZeros
avg_fee_rate has 126056 (15.8%) zerosZeros
total_fees has 125786 (15.7%) zerosZeros
fee_range_min has 388999 (48.6%) zerosZeros
fee_range_max has 125902 (15.7%) zerosZeros
input_count has 89520 (11.2%) zerosZeros
output_amount has 89527 (11.2%) zerosZeros

Reproduction

Analysis started2023-08-22 15:52:46.365470
Analysis finished2023-08-22 15:53:33.967075
Duration47.6 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

height
Real number (ℝ)

HIGH CORRELATION  UNIFORM  UNIQUE 

Distinct800214
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean400107.5
Minimum1
Maximum800214
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size6.1 MiB
2023-08-22T10:53:34.063815image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile40011.65
Q1200054.25
median400107.5
Q3600160.75
95-th percentile760203.35
Maximum800214
Range800213
Interquartile range (IQR)400106.5

Descriptive statistics

Standard deviation231002.03
Coefficient of variation (CV)0.57734991
Kurtosis-1.2
Mean400107.5
Median Absolute Deviation (MAD)200053.5
Skewness1.7739415 × 10-15
Sum3.2017162 × 1011
Variance5.3361937 × 1010
MonotonicityStrictly increasing
2023-08-22T10:53:34.274872image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1 1
 
< 0.1%
533494 1
 
< 0.1%
533472 1
 
< 0.1%
533473 1
 
< 0.1%
533474 1
 
< 0.1%
533475 1
 
< 0.1%
533476 1
 
< 0.1%
533477 1
 
< 0.1%
533478 1
 
< 0.1%
533479 1
 
< 0.1%
Other values (800204) 800204
> 99.9%
ValueCountFrequency (%)
1 1
< 0.1%
2 1
< 0.1%
3 1
< 0.1%
4 1
< 0.1%
5 1
< 0.1%
6 1
< 0.1%
7 1
< 0.1%
8 1
< 0.1%
9 1
< 0.1%
10 1
< 0.1%
ValueCountFrequency (%)
800214 1
< 0.1%
800213 1
< 0.1%
800212 1
< 0.1%
800211 1
< 0.1%
800210 1
< 0.1%
800209 1
< 0.1%
800208 1
< 0.1%
800207 1
< 0.1%
800206 1
< 0.1%
800205 1
< 0.1%

timestamp
Real number (ℝ)

HIGH CORRELATION 

Distinct799869
Distinct (%)> 99.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.4600664 × 1012
Minimum1.2314697 × 1012
Maximum1.6902906 × 1012
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size6.1 MiB
2023-08-22T10:53:34.418905image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1.2314697 × 1012
5-th percentile1.266066 × 1012
Q11.3483451 × 1012
median1.4564766 × 1012
Q31.5715458 × 1012
95-th percentile1.6666783 × 1012
Maximum1.6902906 × 1012
Range4.5882089 × 1011
Interquartile range (IQR)2.232007 × 1011

Descriptive statistics

Standard deviation1.2980655 × 1011
Coefficient of variation (CV)0.088904552
Kurtosis-1.1912838
Mean1.4600664 × 1012
Median Absolute Deviation (MAD)1.11528 × 1011
Skewness0.056660869
Sum1.1683656 × 1018
Variance1.684974 × 1022
MonotonicityNot monotonic
2023-08-22T10:53:34.552935image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1.484947671 × 10122
 
< 0.1%
1.288323362 × 10122
 
< 0.1%
1.614289092 × 10122
 
< 0.1%
1.413427059 × 10122
 
< 0.1%
1.529715264 × 10122
 
< 0.1%
1.664173771 × 10122
 
< 0.1%
1.270850016 × 10122
 
< 0.1%
1.274778288 × 10122
 
< 0.1%
1.675752662 × 10122
 
< 0.1%
1.485728817 × 10122
 
< 0.1%
Other values (799859) 800194
> 99.9%
ValueCountFrequency (%)
1.231469665 × 10121
< 0.1%
1.231469744 × 10121
< 0.1%
1.231470173 × 10121
< 0.1%
1.231470988 × 10121
< 0.1%
1.231471428 × 10121
< 0.1%
1.231471789 × 10121
< 0.1%
1.231472369 × 10121
< 0.1%
1.231472743 × 10121
< 0.1%
1.231473279 × 10121
< 0.1%
1.231473952 × 10121
< 0.1%
ValueCountFrequency (%)
1.690290559 × 10121
< 0.1%
1.690290103 × 10121
< 0.1%
1.690289642 × 10121
< 0.1%
1.690288028 × 10121
< 0.1%
1.690287219 × 10121
< 0.1%
1.690286847 × 10121
< 0.1%
1.690286207 × 10121
< 0.1%
1.690285469 × 10121
< 0.1%
1.690285289 × 10121
< 0.1%
1.690284637 × 10121
< 0.1%

size
Real number (ℝ)

HIGH CORRELATION 

Distinct445651
Distinct (%)55.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean623126.34
Minimum176
Maximum3978938
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size6.1 MiB
2023-08-22T10:53:34.685965image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum176
5-th percentile216
Q132695
median427808
Q31132182.5
95-th percentile1553785.4
Maximum3978938
Range3978762
Interquartile range (IQR)1099487.5

Descriptive statistics

Standard deviation596271.97
Coefficient of variation (CV)0.95690381
Kurtosis-0.36719658
Mean623126.34
Median Absolute Deviation (MAD)427592
Skewness0.57365951
Sum4.9863442 × 1011
Variance3.5554026 × 1011
MonotonicityNot monotonic
2023-08-22T10:53:34.831990image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
216 55546
 
6.9%
215 25417
 
3.2%
473 1839
 
0.2%
474 1686
 
0.2%
217 1381
 
0.2%
374 821
 
0.1%
373 665
 
0.1%
472 656
 
0.1%
475 598
 
0.1%
731 517
 
0.1%
Other values (445641) 711088
88.9%
ValueCountFrequency (%)
176 1
 
< 0.1%
181 2
 
< 0.1%
183 96
< 0.1%
184 13
 
< 0.1%
185 8
 
< 0.1%
186 1
 
< 0.1%
187 8
 
< 0.1%
188 31
 
< 0.1%
189 4
 
< 0.1%
190 13
 
< 0.1%
ValueCountFrequency (%)
3978938 1
< 0.1%
3955272 1
< 0.1%
3952315 1
< 0.1%
3950126 1
< 0.1%
3942952 1
< 0.1%
3939017 1
< 0.1%
3937095 1
< 0.1%
3936545 1
< 0.1%
3935583 1
< 0.1%
3934367 1
< 0.1%

tx_count
Real number (ℝ)

HIGH CORRELATION 

Distinct5451
Distinct (%)0.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1086.8619
Minimum1
Maximum12239
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size6.1 MiB
2023-08-22T10:53:34.972021image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q166
median657
Q32065
95-th percentile2981
Maximum12239
Range12238
Interquartile range (IQR)1999

Descriptive statistics

Standard deviation1093.2861
Coefficient of variation (CV)1.0059108
Kurtosis-0.66401576
Mean1086.8619
Median Absolute Deviation (MAD)655
Skewness0.68539057
Sum8.6972208 × 108
Variance1195274.5
MonotonicityNot monotonic
2023-08-22T10:53:35.111053image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1 89520
 
11.2%
2 12768
 
1.6%
3 6523
 
0.8%
12 4746
 
0.6%
4 4324
 
0.5%
11 4052
 
0.5%
10 3871
 
0.5%
9 3535
 
0.4%
5 3437
 
0.4%
8 3303
 
0.4%
Other values (5441) 664135
83.0%
ValueCountFrequency (%)
1 89520
11.2%
2 12768
 
1.6%
3 6523
 
0.8%
4 4324
 
0.5%
5 3437
 
0.4%
6 2999
 
0.4%
7 3008
 
0.4%
8 3303
 
0.4%
9 3535
 
0.4%
10 3871
 
0.5%
ValueCountFrequency (%)
12239 1
< 0.1%
9647 1
< 0.1%
7434 1
< 0.1%
7426 1
< 0.1%
7323 1
< 0.1%
7295 1
< 0.1%
7264 1
< 0.1%
7215 1
< 0.1%
7197 1
< 0.1%
7196 1
< 0.1%

difficulty
Real number (ℝ)

HIGH CORRELATION 

Distinct382
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean7.1985668 × 1012
Minimum1
Maximum5.3911173 × 1013
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size6.1 MiB
2023-08-22T10:53:35.247092image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1.8186485
Q12864140.5
median1.584272 × 1011
Q31.2759819 × 1013
95-th percentile3.4244332 × 1013
Maximum5.3911173 × 1013
Range5.3911173 × 1013
Interquartile range (IQR)1.2759817 × 1013

Descriptive statistics

Standard deviation1.2006497 × 1013
Coefficient of variation (CV)1.667901
Kurtosis2.7661941
Mean7.1985668 × 1012
Median Absolute Deviation (MAD)1.584272 × 1011
Skewness1.836302
Sum5.7603939 × 1018
Variance1.4415597 × 1026
MonotonicityNot monotonic
2023-08-22T10:53:35.384123image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1 32255
 
4.0%
6.389316884 × 10122016
 
0.3%
5.106422925 × 10122016
 
0.3%
5.646403852 × 10122016
 
0.3%
6.653303141 × 10122016
 
0.3%
7.184404943 × 10122016
 
0.3%
7.182852314 × 10122016
 
0.3%
7.454968648 × 10122016
 
0.3%
7.152633352 × 10122016
 
0.3%
7.019199231 × 10122016
 
0.3%
Other values (372) 749815
93.7%
ValueCountFrequency (%)
1 32255
4.0%
1.182899534 2016
 
0.3%
1.305062132 2016
 
0.3%
1.344224971 2016
 
0.3%
1.818648536 2016
 
0.3%
2.527738215 2016
 
0.3%
3.781179252 2016
 
0.3%
4.53108175 2016
 
0.3%
4.56516291 2016
 
0.3%
6.085476906 2016
 
0.3%
ValueCountFrequency (%)
5.3911173 × 10131879
0.2%
5.235043946 × 10132016
0.3%
5.123433886 × 10132016
0.3%
5.064620643 × 10132016
0.3%
4.954970318 × 10132016
0.3%
4.871240595 × 10132016
0.3%
4.800553431 × 10132016
0.3%
4.788776434 × 10132016
0.3%
4.684340029 × 10132016
0.3%
4.355172221 × 10132016
0.3%

median_fee_rate
Real number (ℝ)

HIGH CORRELATION  SKEWED  ZEROS 

Distinct1614
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean55.258466
Minimum0
Maximum1029502
Zeros178585
Zeros (%)22.3%
Negative0
Negative (%)0.0%
Memory size6.1 MiB
2023-08-22T10:53:35.520154image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q11
median14
Q351
95-th percentile181
Maximum1029502
Range1029502
Interquartile range (IQR)50

Descriptive statistics

Standard deviation1208.2805
Coefficient of variation (CV)21.865979
Kurtosis660496.56
Mean55.258466
Median Absolute Deviation (MAD)14
Skewness783.50115
Sum44218598
Variance1459941.7
MonotonicityNot monotonic
2023-08-22T10:53:35.657185image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 178585
 
22.3%
1 25178
 
3.1%
3 24088
 
3.0%
5 21778
 
2.7%
2 20899
 
2.6%
26 19341
 
2.4%
10 18913
 
2.4%
4 14380
 
1.8%
12 13997
 
1.7%
6 13527
 
1.7%
Other values (1604) 449528
56.2%
ValueCountFrequency (%)
0 178585
22.3%
1 25178
 
3.1%
2 20899
 
2.6%
3 24088
 
3.0%
4 14380
 
1.8%
5 21778
 
2.7%
5.661764706 1
 
< 0.1%
5.702752294 1
 
< 0.1%
5.720588235 1
 
< 0.1%
5.731663685 1
 
< 0.1%
ValueCountFrequency (%)
1029502 1
< 0.1%
241545 1
< 0.1%
68042 1
< 0.1%
56155 1
< 0.1%
50932 1
< 0.1%
38315 1
< 0.1%
36779 1
< 0.1%
28571 1
< 0.1%
26449 1
< 0.1%
26288 1
< 0.1%

avg_fee_rate
Real number (ℝ)

HIGH CORRELATION  SKEWED  ZEROS 

Distinct3198
Distinct (%)0.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean93.791834
Minimum0
Maximum1143052
Zeros126056
Zeros (%)15.8%
Negative0
Negative (%)0.0%
Memory size6.1 MiB
2023-08-22T10:53:35.789215image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q110
median29
Q383
95-th percentile290
Maximum1143052
Range1143052
Interquartile range (IQR)73

Descriptive statistics

Standard deviation1890.674
Coefficient of variation (CV)20.158195
Kurtosis235535.02
Mean93.791834
Median Absolute Deviation (MAD)26
Skewness447.09656
Sum75053539
Variance3574648.3
MonotonicityNot monotonic
2023-08-22T10:53:35.926246image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 126056
 
15.8%
10 11936
 
1.5%
11 11809
 
1.5%
9 11655
 
1.5%
8 11319
 
1.4%
12 11276
 
1.4%
13 10948
 
1.4%
14 10823
 
1.4%
25 10638
 
1.3%
15 10601
 
1.3%
Other values (3188) 573153
71.6%
ValueCountFrequency (%)
0 126056
15.8%
1 2312
 
0.3%
2 4581
 
0.6%
3 5948
 
0.7%
4 6691
 
0.8%
5 7858
 
1.0%
6 8906
 
1.1%
7 10259
 
1.3%
8 11319
 
1.4%
9 11655
 
1.5%
ValueCountFrequency (%)
1143052 1
< 0.1%
881023 1
< 0.1%
467224 1
< 0.1%
460010 1
< 0.1%
224045 1
< 0.1%
197623 1
< 0.1%
188909 1
< 0.1%
168952 1
< 0.1%
138642 1
< 0.1%
133415 1
< 0.1%

total_fees
Real number (ℝ)

HIGH CORRELATION  SKEWED  ZEROS 

Distinct585892
Distinct (%)73.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean33592354
Minimum0
Maximum2.9153275 × 1010
Zeros125786
Zeros (%)15.7%
Negative0
Negative (%)0.0%
Memory size6.1 MiB
2023-08-22T10:53:36.131292image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q11900000
median10402561
Q331619960
95-th percentile1.4498261 × 108
Maximum2.9153275 × 1010
Range2.9153275 × 1010
Interquartile range (IQR)29719960

Descriptive statistics

Standard deviation92056506
Coefficient of variation (CV)2.7404006
Kurtosis18410.23
Mean33592354
Median Absolute Deviation (MAD)10352561
Skewness79.187142
Sum2.6881072 × 1013
Variance8.4744003 × 1015
MonotonicityNot monotonic
2023-08-22T10:53:36.265580image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 125786
 
15.7%
1000000 4553
 
0.6%
2000000 2484
 
0.3%
50000 2440
 
0.3%
100000 1819
 
0.2%
3000000 1635
 
0.2%
150000 1371
 
0.2%
4000000 1195
 
0.1%
200000 1118
 
0.1%
250000 1001
 
0.1%
Other values (585882) 656812
82.1%
ValueCountFrequency (%)
0 125786
15.7%
1 25
 
< 0.1%
2 8
 
< 0.1%
3 1
 
< 0.1%
4 1
 
< 0.1%
5 3
 
< 0.1%
6 1
 
< 0.1%
10 46
 
< 0.1%
19 1
 
< 0.1%
20 6
 
< 0.1%
ValueCountFrequency (%)
2.91532751 × 10101
< 0.1%
2.002260022 × 10101
< 0.1%
1.718464952 × 10101
< 0.1%
1.20013 × 10101
< 0.1%
1.1149075 × 10101
< 0.1%
1.046829921 × 10101
< 0.1%
9480616665 1
< 0.1%
8621369888 1
< 0.1%
8597417976 1
< 0.1%
8591784592 1
< 0.1%

fee_range_min
Real number (ℝ)

HIGH CORRELATION  SKEWED  ZEROS 

Distinct8936
Distinct (%)1.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean9.3849312
Minimum0
Maximum56155
Zeros388999
Zeros (%)48.6%
Negative0
Negative (%)0.0%
Memory size6.1 MiB
2023-08-22T10:53:36.391633image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median1
Q31
95-th percentile23
Maximum56155
Range56155
Interquartile range (IQR)1

Descriptive statistics

Standard deviation148.22171
Coefficient of variation (CV)15.793585
Kurtosis28708.829
Mean9.3849312
Median Absolute Deviation (MAD)1
Skewness100.8945
Sum7509953.4
Variance21969.676
MonotonicityNot monotonic
2023-08-22T10:53:36.514673image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 388999
48.6%
1 231832
29.0%
2 28942
 
3.6%
5 22682
 
2.8%
10 18802
 
2.3%
3 13592
 
1.7%
4 9677
 
1.2%
20 6069
 
0.8%
6 4445
 
0.6%
8 3645
 
0.5%
Other values (8926) 71529
 
8.9%
ValueCountFrequency (%)
0 388999
48.6%
1 231832
29.0%
1.001127396 1
 
< 0.1%
1.001536098 1
 
< 0.1%
1.001636661 1
 
< 0.1%
1.002364066 1
 
< 0.1%
1.002624672 1
 
< 0.1%
1.002635046 1
 
< 0.1%
1.002695418 1
 
< 0.1%
1.002967359 2
 
< 0.1%
ValueCountFrequency (%)
56155 1
< 0.1%
28571 1
< 0.1%
22321 1
< 0.1%
12875 1
< 0.1%
11389 1
< 0.1%
8968 1
< 0.1%
8482 1
< 0.1%
7751 1
< 0.1%
6693 1
< 0.1%
6329 1
< 0.1%

fee_range_max
Real number (ℝ)

HIGH CORRELATION  SKEWED  ZEROS 

Distinct21711
Distinct (%)2.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2160.2088
Minimum0
Maximum49333333
Zeros125902
Zeros (%)15.7%
Negative0
Negative (%)0.0%
Memory size6.1 MiB
2023-08-22T10:53:36.645703image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q1224
median474
Q31321
95-th percentile4484
Maximum49333333
Range49333333
Interquartile range (IQR)1097

Descriptive statistics

Standard deviation85562.366
Coefficient of variation (CV)39.608378
Kurtosis163442.11
Mean2160.2088
Median Absolute Deviation (MAD)410
Skewness349.10063
Sum1.7286293 × 109
Variance7.3209185 × 109
MonotonicityNot monotonic
2023-08-22T10:53:36.780758image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 125902
 
15.7%
444 25136
 
3.1%
389 14034
 
1.8%
442 13710
 
1.7%
222 11264
 
1.4%
3875 10222
 
1.3%
523 9384
 
1.2%
3891 9305
 
1.2%
387 9106
 
1.1%
469 6925
 
0.9%
Other values (21701) 565226
70.6%
ValueCountFrequency (%)
0 125902
15.7%
1 17
 
< 0.1%
2 17
 
< 0.1%
3 24
 
< 0.1%
4 15
 
< 0.1%
5 25
 
< 0.1%
6 20
 
< 0.1%
7 17
 
< 0.1%
8 15
 
< 0.1%
9 12
 
< 0.1%
ValueCountFrequency (%)
49333333 1
< 0.1%
24720498 1
< 0.1%
23747800 1
< 0.1%
20748634 1
< 0.1%
16359918 1
< 0.1%
14836520 1
< 0.1%
11823841 1
< 0.1%
11366166 1
< 0.1%
10785992 1
< 0.1%
10374317 1
< 0.1%

input_count
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct10912
Distinct (%)1.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2847.7516
Minimum0
Maximum20894
Zeros89520
Zeros (%)11.2%
Negative0
Negative (%)0.0%
Memory size6.1 MiB
2023-08-22T10:53:36.911799image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q1149
median1988
Q35343
95-th percentile7042
Maximum20894
Range20894
Interquartile range (IQR)5194

Descriptive statistics

Standard deviation2677.1763
Coefficient of variation (CV)0.94010174
Kurtosis-1.2220132
Mean2847.7516
Median Absolute Deviation (MAD)1988
Skewness0.40008404
Sum2.2788107 × 109
Variance7167272.7
MonotonicityNot monotonic
2023-08-22T10:53:37.040822image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 89520
 
11.2%
1 9793
 
1.2%
2 5103
 
0.6%
12 4175
 
0.5%
13 4127
 
0.5%
3 3301
 
0.4%
14 3213
 
0.4%
15 2858
 
0.4%
4 2503
 
0.3%
11 2354
 
0.3%
Other values (10902) 673267
84.1%
ValueCountFrequency (%)
0 89520
11.2%
1 9793
 
1.2%
2 5103
 
0.6%
3 3301
 
0.4%
4 2503
 
0.3%
5 2141
 
0.3%
6 1646
 
0.2%
7 1358
 
0.2%
8 1281
 
0.2%
9 1167
 
0.1%
ValueCountFrequency (%)
20894 1
< 0.1%
20881 1
< 0.1%
20848 1
< 0.1%
20837 1
< 0.1%
20817 2
< 0.1%
20805 1
< 0.1%
20796 2
< 0.1%
15982 1
< 0.1%
15294 1
< 0.1%
14679 1
< 0.1%

output_count
Real number (ℝ)

HIGH CORRELATION 

Distinct15811
Distinct (%)2.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean3053.4679
Minimum1
Maximum23642
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size6.1 MiB
2023-08-22T10:53:37.164861image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q1163
median1784
Q35443
95-th percentile9022
Maximum23642
Range23641
Interquartile range (IQR)5280

Descriptive statistics

Standard deviation3230.4213
Coefficient of variation (CV)1.0579516
Kurtosis0.60269597
Mean3053.4679
Median Absolute Deviation (MAD)1782
Skewness1.005808
Sum2.4434278 × 109
Variance10435622
MonotonicityNot monotonic
2023-08-22T10:53:37.293916image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1 87990
 
11.0%
3 8238
 
1.0%
2 7027
 
0.9%
5 3507
 
0.4%
4 3002
 
0.4%
7 2207
 
0.3%
23 2014
 
0.3%
17 1966
 
0.2%
21 1947
 
0.2%
22 1924
 
0.2%
Other values (15801) 680392
85.0%
ValueCountFrequency (%)
1 87990
11.0%
2 7027
 
0.9%
3 8238
 
1.0%
4 3002
 
0.4%
5 3507
 
0.4%
6 1839
 
0.2%
7 2207
 
0.3%
8 1563
 
0.2%
9 1701
 
0.2%
10 1311
 
0.2%
ValueCountFrequency (%)
23642 1
< 0.1%
23586 1
< 0.1%
23523 1
< 0.1%
22881 1
< 0.1%
22805 1
< 0.1%
22425 1
< 0.1%
22418 1
< 0.1%
22283 1
< 0.1%
22247 1
< 0.1%
21919 1
< 0.1%

output_amount
Real number (ℝ)

HIGH CORRELATION  SKEWED  ZEROS 

Distinct695035
Distinct (%)86.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.0473522 × 1012
Minimum0
Maximum6.4993243 × 1014
Zeros89527
Zeros (%)11.2%
Negative0
Negative (%)0.0%
Memory size6.1 MiB
2023-08-22T10:53:37.421948image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q18.2411603 × 1010
median4.0231208 × 1011
Q31.1386913 × 1012
95-th percentile3.6610259 × 1012
Maximum6.4993243 × 1014
Range6.4993243 × 1014
Interquartile range (IQR)1.0562797 × 1012

Descriptive statistics

Standard deviation3.4963679 × 1012
Coefficient of variation (CV)3.3382925
Kurtosis6363.5245
Mean1.0473522 × 1012
Median Absolute Deviation (MAD)3.8401577 × 1011
Skewness56.517856
Sum8.3810586 × 1017
Variance1.2224588 × 1025
MonotonicityNot monotonic
2023-08-22T10:53:37.557991image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 89527
 
11.2%
5000000000 2825
 
0.4%
1 × 1010835
 
0.1%
5000000 361
 
< 0.1%
1.5 × 1010347
 
< 0.1%
2 × 1010262
 
< 0.1%
100000000 260
 
< 0.1%
500000000 213
 
< 0.1%
1000000 184
 
< 0.1%
10000000 177
 
< 0.1%
Other values (695025) 705223
88.1%
ValueCountFrequency (%)
0 89527
11.2%
1 1
 
< 0.1%
6000 3
 
< 0.1%
10000 1
 
< 0.1%
21939 1
 
< 0.1%
50000 1
 
< 0.1%
57938 1
 
< 0.1%
90192 1
 
< 0.1%
100000 1
 
< 0.1%
117127 1
 
< 0.1%
ValueCountFrequency (%)
6.499324335 × 10141
< 0.1%
5.691036471 × 10141
< 0.1%
5.2283214 × 10141
< 0.1%
5.204130183 × 10141
< 0.1%
4.499688523 × 10141
< 0.1%
4.376016742 × 10141
< 0.1%
4.00553845 × 10141
< 0.1%
3.782961374 × 10141
< 0.1%
3.641505161 × 10141
< 0.1%
3.584723975 × 10141
< 0.1%

Interactions

2023-08-22T10:53:29.631614image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-08-22T10:53:01.411134image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-08-22T10:53:03.783141image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-08-22T10:53:06.157116image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-08-22T10:53:08.629116image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-08-22T10:53:11.031654image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-08-22T10:53:13.310614image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-08-22T10:53:15.643872image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-08-22T10:53:18.019473image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-08-22T10:53:20.327749image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-08-22T10:53:22.614469image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-08-22T10:53:24.967791image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-08-22T10:53:27.286324image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-08-22T10:53:29.844662image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-08-22T10:53:01.607522image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-08-22T10:53:03.964182image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-08-22T10:53:06.362162image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-08-22T10:53:08.844160image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-08-22T10:53:11.200734image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-08-22T10:53:13.495656image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-08-22T10:53:15.840908image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-08-22T10:53:18.192519image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-08-22T10:53:20.524793image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-08-22T10:53:22.802881image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-08-22T10:53:25.140839image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-08-22T10:53:27.488362image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-08-22T10:53:30.029694image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-08-22T10:53:01.787562image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-08-22T10:53:04.132221image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-08-22T10:53:06.549205image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-08-22T10:53:09.025201image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-08-22T10:53:11.354767image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-08-22T10:53:13.668703image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-08-22T10:53:16.017947image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-08-22T10:53:18.359563image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-08-22T10:53:20.706834image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-08-22T10:53:22.970918image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-08-22T10:53:25.325871image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-08-22T10:53:27.668161image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-08-22T10:53:30.201733image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-08-22T10:53:02.015614image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-08-22T10:53:04.302259image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-08-22T10:53:06.757242image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-08-22T10:53:09.208252image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-08-22T10:53:11.533809image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-08-22T10:53:13.858761image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-08-22T10:53:16.188986image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-08-22T10:53:18.549144image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-08-22T10:53:20.907870image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-08-22T10:53:23.155960image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-08-22T10:53:25.513914image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-08-22T10:53:27.874207image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-08-22T10:53:30.362779image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-08-22T10:53:02.181651image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-08-22T10:53:04.477298image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-08-22T10:53:06.939290image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-08-22T10:53:09.372289image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-08-22T10:53:11.717841image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-08-22T10:53:14.019820image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-08-22T10:53:16.381029image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-08-22T10:53:18.723156image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-08-22T10:53:21.083918image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-08-22T10:53:23.412448image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-08-22T10:53:25.693964image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-08-22T10:53:28.040245image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-08-22T10:53:30.537809image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-08-22T10:53:02.346689image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-08-22T10:53:04.653338image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-08-22T10:53:07.103326image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-08-22T10:53:09.548320image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-08-22T10:53:11.909886image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-08-22T10:53:14.172867image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-08-22T10:53:16.567071image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-08-22T10:53:18.978130image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-08-22T10:53:21.250957image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-08-22T10:53:23.605492image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-08-22T10:53:25.881006image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-08-22T10:53:28.204291image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-08-22T10:53:30.711849image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-08-22T10:53:02.525729image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-08-22T10:53:04.848382image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-08-22T10:53:07.291368image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-08-22T10:53:09.731370image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-08-22T10:53:12.078448image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-08-22T10:53:14.405943image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-08-22T10:53:16.749113image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-08-22T10:53:19.145118image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-08-22T10:53:21.418995image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-08-22T10:53:23.774530image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-08-22T10:53:26.057046image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-08-22T10:53:28.383322image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-08-22T10:53:30.892889image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-08-22T10:53:02.710772image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-08-22T10:53:05.014420image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-08-22T10:53:07.506418image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-08-22T10:53:09.978417image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-08-22T10:53:12.258513image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-08-22T10:53:14.576006image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-08-22T10:53:16.936155image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-08-22T10:53:19.307155image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-08-22T10:53:21.603028image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-08-22T10:53:23.941559image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-08-22T10:53:26.237087image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-08-22T10:53:28.634389image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-08-22T10:53:31.056927image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-08-22T10:53:02.896814image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-08-22T10:53:05.187459image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-08-22T10:53:07.699046image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-08-22T10:53:10.152456image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-08-22T10:53:12.445560image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-08-22T10:53:14.775076image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-08-22T10:53:17.107253image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-08-22T10:53:19.483467image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-08-22T10:53:21.770278image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-08-22T10:53:24.107596image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-08-22T10:53:26.416127image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-08-22T10:53:28.820421image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-08-22T10:53:31.218964image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-08-22T10:53:03.060850image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-08-22T10:53:05.360936image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-08-22T10:53:07.883476image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-08-22T10:53:10.314493image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-08-22T10:53:12.631606image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-08-22T10:53:14.968118image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-08-22T10:53:17.271281image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-08-22T10:53:19.652542image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-08-22T10:53:21.940317image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-08-22T10:53:24.276644image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-08-22T10:53:26.595168image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-08-22T10:53:28.980457image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-08-22T10:53:31.379000image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-08-22T10:53:03.228890image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-08-22T10:53:05.539967image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-08-22T10:53:08.064521image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-08-22T10:53:10.488542image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-08-22T10:53:12.802476image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-08-22T10:53:15.129160image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-08-22T10:53:17.469331image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-08-22T10:53:19.829574image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-08-22T10:53:22.105363image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-08-22T10:53:24.447673image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-08-22T10:53:26.771201image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-08-22T10:53:29.145504image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-08-22T10:53:31.543036image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-08-22T10:53:03.416940image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-08-22T10:53:05.712007image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-08-22T10:53:08.233778image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-08-22T10:53:10.667582image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-08-22T10:53:12.974521image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-08-22T10:53:15.295199image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-08-22T10:53:17.653372image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-08-22T10:53:20.003620image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-08-22T10:53:22.265399image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-08-22T10:53:24.612710image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-08-22T10:53:26.938245image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-08-22T10:53:29.297528image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-08-22T10:53:31.706074image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-08-22T10:53:03.597972image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-08-22T10:53:05.970064image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-08-22T10:53:08.422069image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-08-22T10:53:10.867627image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-08-22T10:53:13.133579image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-08-22T10:53:15.465261image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-08-22T10:53:17.840427image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-08-22T10:53:20.161656image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-08-22T10:53:22.431437image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-08-22T10:53:24.775747image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-08-22T10:53:27.105282image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-08-22T10:53:29.457565image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-08-22T10:53:37.657013image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
heighttimestampsizetx_countdifficultymedian_fee_rateavg_fee_ratetotal_feesfee_range_minfee_range_maxinput_countoutput_countoutput_amount
height1.0001.0000.8900.8130.9980.2670.0530.5410.6980.2070.8720.8260.497
timestamp1.0001.0000.8900.8130.9980.2670.0530.5410.6980.2070.8720.8260.497
size0.8900.8901.0000.9260.8910.3830.1880.7350.6850.3650.9780.9290.652
tx_count0.8130.8130.9261.0000.8140.4730.2860.8040.6800.4410.9050.9700.729
difficulty0.9980.9980.8910.8141.0000.2730.0590.5460.7000.2110.8730.8270.495
median_fee_rate0.2670.2670.3830.4730.2731.0000.7860.7550.3610.6020.3740.4630.531
avg_fee_rate0.0530.0530.1880.2860.0590.7861.0000.6660.1860.7920.1810.2720.468
total_fees0.5410.5410.7350.8040.5460.7550.6661.0000.5860.6920.7280.7900.754
fee_range_min0.6980.6980.6850.6800.7000.3610.1860.5861.0000.2440.6730.6670.425
fee_range_max0.2070.2070.3650.4410.2110.6020.7920.6920.2441.0000.3620.4310.581
input_count0.8720.8720.9780.9050.8730.3740.1810.7280.6730.3621.0000.8980.647
output_count0.8260.8260.9290.9700.8270.4630.2720.7900.6670.4310.8981.0000.731
output_amount0.4970.4970.6520.7290.4950.5310.4680.7540.4250.5810.6470.7311.000

Missing values

2023-08-22T10:53:31.854116image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-08-22T10:53:32.452559image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.

Sample

heighttimestampsizetx_countdifficultymedian_fee_rateavg_fee_ratetotal_feesfee_range_minfee_range_maxinput_countoutput_countoutput_amount
01123146966500021511.00.0000.00.0010
12123146974400021511.00.0000.00.0010
23123147017300021511.00.0000.00.0010
34123147098800021511.00.0000.00.0010
45123147142800021511.00.0000.00.0010
56123147178900021511.00.0000.00.0010
67123147236900021511.00.0000.00.0010
78123147274300021511.00.0000.00.0010
89123147327900021511.00.0000.00.0010
910123147395200021511.00.0000.00.0010
heighttimestampsizetx_countdifficultymedian_fee_rateavg_fee_ratetotal_feesfee_range_minfee_range_maxinput_countoutput_countoutput_amount
8002048002051690284637000137974522165.391117e+138.02662513134191557.004032469.973890483718017578221716647
8002058002061690285289000169888032245.391117e+137.19337011114869666.000000301.61256575799003337235968158
8002068002071690285469000181741045855.391117e+135.814739770647865.774597235.4166676758852260514354589
8002078002081690286207000170440340075.391117e+137.13192112125545735.816176696.86411175189518384146426804
8002088002091690286847000137834026575.391117e+138.81531514144788636.151030808.45070469837400419748450315
8002098002101690287219000183362446175.391117e+136.09866810107984185.774597627.94348573788118361865784734
8002108002111690288028000155167724985.391117e+138.31622813131547797.0000001033.05785179376607722603107147
8002118002121690289642000157943138015.391117e+1312.95425421217809689.500000673.0352116964112971327695528497
8002128002131690290103000158022831625.391117e+138.22511314147282737.027581671.291714703710083640226583272
8002138002141690290559000138571421145.391117e+1318.11594217178594047.050000336.65350443515102429616498351